Hire Android Developers in India

Why Does “Works on My Pixel” Tell You Nothing?

This is the structural difference between Android and every other platform you might be hiring for, and it is the one that shows up in your budget rather than in your architecture diagram.

Fragmentation has three axes, and only one of them gets discussed

The first axis is the API level. Google supports a long tail of releases and your users sit across many of them, which means the code path a candidate learned last year may simply not exist on a third of your install base. Every capability discussion turns into a version discussion: this permission model arrived here, that storage behaviour changed there, this notification requirement applies only above a certain level. A competent engineer keeps a mental map of which behaviours are gated by the device's OS version and which are gated by your app's target level, and those are two different things that get confused constantly.

The second axis is hardware. Screen sizes from small phones to tablets to foldables that change size while your Activity is alive, RAM from half a gigabyte on entry-level devices to more than a laptop, storage speed, thermal behaviour, camera pipelines that differ per vendor, and CPUs where a mid-range chip from four years ago is still a current phone in half your markets. Memory is the one that bites hardest: the system kills your process to reclaim RAM, and on a low-memory device it does so far more often, which turns process death from a theoretical edge case into your users' normal experience.

The third axis is the one people skip, and it is the expensive one. Manufacturers ship their own layer on top of AOSP, and that layer changes runtime behaviour in ways the public SDK does not describe.

OEM skins are behavioural forks, not themes

It is tempting to treat One UI, MIUI, ColorOS, Funtouch and the rest as skins, meaning colours and icons. They are not. They modify permission dialogs, notification grouping and priority, default launcher behaviour, deep link handling, split-screen rules, autostart policy and, worst of all, when your process gets killed. There is a community-run site, dontkillmyapp.com, whose entire purpose is documenting which manufacturer breaks background work in which way and which toggle a user has to hunt down to stop it. That such a site has to exist is the clearest statement of the problem I can offer you.

What this means for hiring is concrete. Ask a candidate what they do differently for devices where the vendor kills backgrounded apps aggressively. A weak answer talks about foreground services as though declaring one solves it. A strong answer accepts that some of this is unwinnable in code, and describes the mitigations that actually help: make the work resumable so a kill costs progress rather than correctness, use the server to re-trigger rather than trusting a local schedule, detect the affected vendor and surface an in-app explanation with a deep link into the relevant settings screen, and instrument completion rates per manufacturer so you can see the problem instead of arguing about it.

The test matrix you can actually afford

Nobody tests on everything. The point is to choose deliberately rather than by which phones the team happens to own. Pull your own analytics and rank devices by install base, then by revenue, then by crash concentration. Those three lists rarely agree, and the disagreement is the interesting part: the device that generates the most crashes is frequently not the device that generates the most money, and knowing which one you are optimising for is a product decision your engineer should be asking you about.

Practically, a small physical set covers most of it. One recent flagship, one mid-range device that is two or three years old, one low-RAM device, one tablet or foldable if you support large screens, and one device from whichever manufacturer dominates your largest market. Everything beyond that goes to a cloud device farm. Firebase Test Lab runs your instrumented tests and a crawler on real hardware, and Google Play's pre-launch report does something similar automatically for every build you upload to a testing track, which is free coverage most teams never look at. Deeper matrix work belongs with a dedicated function; our mobile testing services in India page covers how a real device matrix is run and reported.

What fragmentation costs, stated plainly

Budget for it as a recurring percentage of engineering time rather than a one-off hardening sprint. Every feature that touches storage, permissions, notifications, background execution, the camera or the file picker needs verification on more than one vendor, and the annual OS release adds a compatibility pass whether or not you planned one. Teams that treat this as overhead to be eliminated end up shipping features that work for the subset of users who happen to look like the development team. Teams that treat it as the job hire differently and estimate more honestly.

Kotlin, Compose and the View System: What Your Codebase Says About the Hire

Open the repository in front of a candidate before you ask them anything. The first thing they notice, and the first question they ask about it, is worth more than a prepared answer to a prepared question.

A View-system codebase

XML layouts, Activities and Fragments, view binding or the older findViewById, RecyclerView with adapters and view holders, and the Navigation component or a hand-rolled equivalent. This is not legacy in the pejorative sense. It is what the majority of large shipping Android apps still are, it is well understood, and its performance characteristics are known. An engineer fluent here can read a Fragment lifecycle without looking it up, knows why a view holder is recycled and what happens when you forget, and can spot the difference between the Fragment lifecycle and the Fragment's view lifecycle, which is one of the most reliable seniority tells on the platform.

What it implies about the hire: this codebase has accumulated architecture, probably more than one pattern applied at different times, and possibly a screen or two that grew to a couple of thousand lines. That is normal. The person you want has a plan for extracting logic incrementally behind tests, not a proposal to rewrite the screen in Compose because it would be cleaner.

A Compose codebase

Compose is declarative. You describe the interface for a given state and the runtime works out what to redraw. It reached its first stable release in 2021 and it is where Google is putting its UI investment, so a candidate with no Compose exposure at all will hold you back within a year even if your app is entirely XML today.

What to probe is recomposition, because that is where Compose apps go wrong. Ask what causes a composable to recompose and how they would find out that one is recomposing far more often than it should. Ask about state hoisting: which state a composable owns, which is passed in, and which is held somewhere that survives the composition entirely. Ask what happens when they pass an unstable type as a parameter and why that stops the compiler from skipping. Ask how they give items in a lazy list a stable key and what breaks when they do not. And ask whether they have used a baseline profile, because startup and first-scroll performance on mid-range hardware is the single most common complaint about Compose apps and profiles are the standard answer.

One honest caveat worth hearing from a candidate: Compose is not automatically faster than Views, and on very dense scrolling surfaces a well-tuned RecyclerView can still win. Somebody who tells you Compose is faster full stop has read marketing rather than traces.

The half-migrated app, which is most of them

Almost every real Android codebase in 2026 is partway between the two systems, and interop is therefore a genuine skill rather than a curiosity. Compose inside the View world means hosting a composable in a ComposeView placed in an XML layout or created in a Fragment, and getting the composition strategy right so it is disposed when the view goes away. Views inside Compose means wrapping them so that measurement and updates flow correctly. Both directions have sharp edges around sizing, lifecycle mismatch and theme bridging, because your Material theme in XML and your Compose theme are separate things that somebody has to keep in agreement.

Then there is navigation, which is where half-migrated apps hurt most. You end up with the Fragment-based navigation graph and a Compose navigation host in the same app, and a decision to make about which one owns the back stack. There is no elegant answer. There is a decision, made deliberately and written down, or an accident that costs you a week every time somebody adds a screen. Ask a candidate which side they put in charge on their last mixed app and why. If they have never faced it, they have not worked on a migration.

My blunt advice on migration itself: do it screen by screen, driven by which screens you are already changing, and never as a standalone project with its own budget. A rewrite that delivers the same features on a different toolkit is very hard to justify to whoever funds it, and it is the class of project that gets cancelled at sixty percent, leaving you with two half-systems and nobody who remembers the plan.

The Java that is still in there

Google made Kotlin its preferred Android language in 2019, and new APIs, samples and Jetpack libraries have been Kotlin-first ever since. Compose is Kotlin only, because it depends on a compiler plugin. So new code is Kotlin, and that is not really a debate any more.

Old code is another matter. Plenty of production apps still contain substantial Java, and the interop boundary is where the bugs live. Java has no compile-time nullability, so a value crossing into Kotlin as a platform type will happily be null and throw somewhere unrelated to where the mistake was made. Somebody who annotates the Java side first, before writing a line of Kotlin against it, has clearly been bitten by this already. Convert a file when you have a reason to open it anyway. Converting for neatness is how regressions get introduced into code that had been sitting there working for six years.

One more thing to listen for: Kotlin fluency is not Kotlin syntax. Somebody writing Java in Kotlin clothing produces null checks everywhere instead of modelling absence, classes where a sealed hierarchy would remove a whole category of bug, and callbacks where a flow belongs. It reads fine and it ages badly.

What Should You Actually Test in an Android Screening Call?

Six subjects, ten minutes apiece, and not one algorithm puzzle among them. Every subject here corresponds to a bug category that has cost somebody money after a release went out. Take them in the order given: how a candidate handles the first two tells you whether the last four need pressing hard or waving through.

Lifecycle and configuration changes

Start here because it is foundational and because a surprising number of candidates have only memorised the diagram. Ask what happens when the user rotates the phone. The baseline answer is that the Activity is destroyed and recreated by default. The better answer notes that rotation is only one of the triggers, and that switching to dark mode, changing the system font size, changing locale, plugging in a keyboard, or unfolding a foldable all do the same thing.

Then ask about the escape hatch, because everybody eventually meets it. Declaring configuration changes as handled by the Activity stops the recreation, which sounds attractive and is usually a mistake: you have now taken responsibility for updating every resource yourself, and the bugs that follow are subtle ones where the layout is right but a dimension or a string is stale. There are legitimate uses, mostly around media playback and camera sessions. A candidate who reaches for it as a general solution to state loss has treated the symptom.

Finish with the modern answer: a ViewModel survives configuration change because it is scoped above the Activity instance, so the network request in flight is not thrown away and restarted. Ask what else they put there and what they deliberately keep out. Anything holding a reference to a view or an Activity does not belong in a ViewModel, and knowing why leads straight into the memory section below.

Process death and saved state, which is the bug you cannot reproduce

This is my favourite question on the platform because the failure is invisible in normal development and obvious to users. Ask the candidate to explain the difference between a configuration change and process death, and what survives each.

The answer you want: a ViewModel survives a configuration change and does not survive process death, because process death takes the whole process with it. When the user backgrounds your app and the system reclaims memory, your process is killed. When they come back through the recent apps list, Android recreates the task and hands your Activity back the saved instance state Bundle, and everything not in that Bundle is gone. So the search query is empty, the multi-step form has reset to page one, and the user is looking at a screen that has forgotten what they were doing. The correct mechanism is a saved state handle inside the ViewModel, or the saveable variants of remember in Compose, holding the small amount of data needed to reconstruct where the user was. Small is the operative word: the Bundle goes through a transaction with a size limit, and stuffing a list of results into it will throw rather than degrade.

Then ask the question that separates people who know the theory from people who have shipped: how do you test it? The good answer names the developer options toggle that destroys activities as soon as they go into the background, and better still the background process limit setting, which forces the real thing. The best answer mentions that this needs doing on a physical device with the app genuinely backgrounded, because the failure mode is one that never appears on a development machine where the app is always in the foreground and always attached to the debugger. If a candidate has never deliberately induced process death, they have shipped this bug and not known it.

Coroutines and structured concurrency

Ask what structured concurrency buys them over launching work and hoping. The answer should be about scope: a child coroutine cannot outlive its parent scope, so cancelling the scope cancels everything under it, and a screen that goes away takes its in-flight work with it rather than leaking a callback that fires into a dead view.

Then push on cancellation, which is where most people are vague. Cancellation in Kotlin is cooperative. A coroutine sitting in a suspending call from the standard library gets cancelled promptly, but a tight computational loop with no suspension point will keep running to completion after cancellation has been requested, and the candidate should know to check for it explicitly. Ask what happens to code in a finally block during cancellation, and whether a suspending cleanup call inside it will actually run.

The Android-specific follow-up is worth more than either. Ask how they collect a flow in a UI layer. Launching a collection tied to the wrong lifecycle keeps it running while the app is in the background, which means your app is still processing location updates or holding a socket open when the user thinks they have left. The current answer is to restart collection when the lifecycle reaches a started state and stop it when it falls below, and there is a well-known idiom for exposing a hot state flow with a short subscription timeout so that a rotation does not tear down and rebuild the upstream. A candidate who reaches for that idiom unprompted has debugged this in production. Ask also where they choose to move work off the main thread and what belongs on which dispatcher, and listen for whether the answer is a rule they memorised or a decision they make per call.

Memory leaks, and the Context that outlived its screen

Android hands every component a Context, and the difference between the application Context and an Activity Context is the source of a whole family of leaks. An Activity Context is tied to a screen with a view hierarchy attached. Holding it in something that lives longer than the screen keeps the entire hierarchy in memory, and the user experiences that as memory growth on rotation followed by the system killing the app.

Ask where they last leaked one. The shapes are recognisable and hard to invent: a singleton or a companion object holding an Activity Context that was passed in during initialisation, a listener or broadcast receiver registered and never unregistered, a handler posting a delayed message from an inner class that implicitly holds its outer, a thread or timer started in a screen and never stopped, a static reference to a view held for convenience. In Fragments there is a specific and very common one worth naming: a binding reference kept as a property and not cleared when the view is destroyed, because a Fragment can outlive its own view and the binding drags the whole hierarchy along with it.

Then ask how they found it. LeakCanary during development, the memory profiler and a heap dump when they need the retention path, and repeated allocation over a specific user journey rather than staring at a graph. Also worth asking: what they would do about a leak coming out of a third-party SDK, which is a diplomacy problem as much as an engineering one. Note that a leak on Android is not always a crash; often it is a slow degradation that ends in the system terminating the process, which is recorded separately from a crash and therefore missing from the number most teams watch.

Why the list is janky, which is almost always the adapter

Describe a symptom rather than a solution. The feed stutters when scrolled quickly, it is worse on a three-year-old mid-range device, and it looks fine on the team's phones. Ask what they check first. A candidate who immediately starts listing optimisations has failed the question; the right instinct is to record a trace on the affected device and look at what the main thread was doing during the dropped frames.

When they get to causes, the honest answer is that the drawing is rarely the problem. The problem is nearly always in the binding step, and the offenders repeat: an image decoded on the main thread instead of handed to an image loader, a date or currency formatter constructed once per row rather than once, a database read or a preferences read happening inside binding, layout inflation triggered repeatedly because view types churn, an entire list refreshed with a blanket change notification instead of a diffed update so every visible row rebinds, and nested scrolling containers arranged so that recycling is disabled entirely and every row is inflated at once. That last one is worth asking about directly, because it is common and it is invisible in code review.

The Compose equivalent has the same shape and different names: unstable parameters defeating skipping, missing keys so items lose identity, state read at too high a level so a whole screen recomposes when one row changed, and a first scroll that is slow because the code has not been profile-guided. Whichever toolkit your app uses, the signal is method rather than vocabulary. Ask what number they measured, what single change they made, and what the number was afterwards.

Release ownership on Play

Save the closing stretch of the call for this and let it decide any candidate you are considering above mid-level. Have they set up app signing on a project from scratch, and do they understand the difference between the upload key and the signing key that Google holds? Have they run a staged rollout and defined in advance what would make them halt it? Have they had a release rejected or an app pulled for a policy reason, and what did they change? Have they uploaded a mapping file and then read a deobfuscated stack trace from a release build? Have they ever shipped a build that crashed only in release because a minification rule stripped something reflection depended on?

These are yes-or-no questions with stories attached, and the stories contain details nobody invents. It is also the most trustworthy part of assessing somebody at a distance, since a release history leaves evidence and a self-assessment does not.

Play Console Reality: Rollouts, Vitals and the Target API Treadmill

Google Play lets you move faster than Apple does and holds you to account differently. Understanding the trade is most of what separates a developer who ships on Android from one who writes Android code.

Staged rollout, and what halting actually does

You can release an update to a percentage of users and increase it over days while you watch. This is genuinely the best safety mechanism on either mobile platform, and it is free. Use it for anything that is not a trivial copy change, start small enough that a bad build reaches a survivable number of people, and decide the halt criteria before you press the button rather than while the graph is moving.

Halt criteria should be numbers, agreed in advance, that a person on call can act on without a meeting. A crash rate for the new version above an agreed multiple of the previous version's. An ANR rate above its baseline. A drop in a core conversion event. A spike in one-star reviews or in support tickets mentioning a specific screen. Without written criteria, what happens instead is a debate about whether the increase is significant, conducted while the rollout continues.

Be clear about the limits. Halting stops further distribution, and it does not take the build off phones that already have it. Getting those users onto something safe means shipping a fixed build with a higher version code and rolling that out. The good news, and this is the real Android advantage, is that you can do that in a timeframe measured in hours rather than waiting in a review queue. That difference is why Android teams can afford a slightly more aggressive release cadence than their iOS counterparts, and why the same team should not assume the two platforms need the same release process.

Android vitals, and enforcement that is not a human

Play measures user-perceived crash rate and user-perceived ANR rate from real installs and compares them against published bad-behaviour thresholds. Exceed them and the consequences are not a conversation: your store listing can carry a warning, and your discoverability in search and recommendations can be reduced. There are also technical quality standards that affect whether you are surfaced on tablets, foldables and Chromebooks. The exact thresholds and the exact treatment change over time, so read the current Play Console documentation rather than trusting a figure quoted anywhere, including here.

The practical instruction for a hiring manager is simpler than the policy. Ask a candidate what their crash-free and ANR-free rates were on the last app they owned, per version rather than in aggregate, and how they knew. Aggregate figures flatter you, because a healthy old version props up the average while the version you just shipped quietly gets worse. Ask separately about memory terminations, since a process killed for using too much memory is not recorded as a crash and an app can therefore show excellent crash figures while being force-closed constantly on cheaper devices. Users describe that as the app shutting itself down, and they say so in reviews.

The target API level requirement, and why the number is not printed here

Google Play requires that new apps and updates target a recent API level, and that requirement moves upward on an annual cycle. Apps that fall behind stop being available to new users on newer devices, which is a slow, quiet loss rather than a dramatic removal. I am deliberately not printing the current level or the current deadline, because both change every year and a stale number on a web page is worse than no number. Check the current requirement in the Play Console documentation at the time you plan the work.

The mechanism is what you should hire against. Raising your target level opts your app into that release's behaviour changes, all at once, and some of those will break you. Historically these have included storage access, background execution, permission handling, exact alarm access, notification permission and foreground service declarations. So the annual compliance pass is not a manifest edit; it is a read of the behaviour change list, a set of code changes, and a testing round on devices running that OS version. Teams that treat it as paperwork discover in the last fortnight that a core flow no longer works. Ask a candidate to describe the last target level bump they did and what it broke. If the answer is nothing, they either had a very simple app or they were not the one doing it.

Signing, bundles and the key you must not lose

Publishing on Play means shipping an Android App Bundle rather than an APK, and Play generates the device-specific artifacts. Play App Signing means Google holds the key used to sign what users install, and you hold an upload key used only to authenticate what you send. This is a meaningful safety improvement, because an upload key can be replaced through support if it is compromised, whereas a self-managed signing key that gets lost ends the app permanently: there is no route to publishing an update that users can install over the old one.

The related trap is minification. R8 shrinks and obfuscates your release build, which means release builds can crash in ways debug builds never do, typically where reflection or serialisation depends on names that were renamed. The mapping file produced by each build is what turns a release stack trace back into readable code, and it has to be uploaded and retained per version. Ask what a candidate does when a top crash comes back unreadable. A shrug there means your first serious production incident is diagnosed by guesswork.

Tracks, testing and the review that is lighter than you think

Play stacks its distribution tracks: an internal one for the team, a closed one for an invited group, an open one anybody can join, then production. Each stage is worth using, and the pre-launch report that runs uploaded builds on real hardware is free feedback most teams ignore. Google has also added pre-launch testing requirements for some categories of new developer account, so check the current rules before you assume you can publish on day one.

Review itself is lighter and faster than Apple's, and that is a trap rather than a gift. Enforcement on Play is largely automated and frequently retroactive: policy sweeps can pull down an app that has been live for years, declarations about permissions and data can be audited later, and enforcement can reach the developer account rather than only the app. The worst outcome on Android is not a rejection before launch. It is a suspension after launch, with your users unable to install, while you work out which SDK caused it. That risk is why the dependency inventory work described below is not optional.

When Hiring Native Android Is the Wrong Answer

Better to say this now than four months into an engagement. There are common situations where a native Android hire is an expensive answer to a question you have not asked precisely enough.

Screens over an API, on both platforms, with a small team

If the app is lists, forms, detail views, sign-in and a checkout, and you need it on Android and iOS, then hiring natively for each means two engineers, two codebases, two release processes and every bug fixed twice. One cross-platform engineer will usually deliver more product per month, and on that class of app the quality difference is much smaller than platform partisans claim. The threshold question is what proportion of your app is genuinely platform-specific rather than what proportion feels special.

Where Flutter earns its keep, and what it costs

Flutter paints every control itself instead of handing that job to the platform, which is why an app looks identical on both stores and why it can feel a little foreign on Android if nobody tunes it. That is a design cost you can manage. The real costs sit elsewhere: when Android ships a new system behaviour or a new UI convention you wait for the framework rather than adopting it immediately, anything platform-specific means writing a plugin in Kotlin anyway, and system surfaces such as home screen widgets and rich notification handling are native work regardless of what the main app is written in. If your product is mostly content and forms and you want both stores served by one team, that trade is usually worth taking. Our Flutter developers in India page argues the same trade from the other direction.

Where React Native fits, and where the bill arrives

React Native is the strongest choice when you already employ React engineers, because the language, the tooling, the state management and much of the business logic carry across, and that transfer is worth a great deal in practice. Two costs to plan for. Major version upgrades have historically been more work than teams expect, especially with a long tail of community native modules that each have to catch up. And when performance degrades, it usually degrades where JavaScript meets the native layer, which happens to be the one place a JavaScript specialist can see least. You end up buying Android expertise for the hardest problems regardless. If that describes your situation, the React Native developers in India page is the more useful read.

The list that settles the argument

Write down every feature that touches hardware, background execution, a system integration, the notification tray, a home screen widget, or a permission that needs explaining to the user. If the list is short, go cross-platform and hire native later for the specific pieces. If it is long, you were always going to need this hire, and starting cross-platform only defers the cost to the point where it is most expensive to pay. Most products that get large enough end up mixed anyway, and choosing that deliberately at the start is far cheaper than arriving there after two rewrites.

Four Positions Companies Arrive In, and What Each One Should Be Briefed As

Each of these is a composite, assembled out of the kinds of request that reach us rather than an account of any named client. Pick whichever of the four sits closest to your own position.

The app that only misbehaves in one country

Your crash and support data cluster around one region, and when you look closer they cluster around two or three manufacturers that barely register in your home market. Notifications arrive hours late or not at all, the overnight sync has silently stopped for a slice of users, and nobody can reproduce any of it on the phones in the office.

This is a fragmentation and background execution engagement rather than a feature one. The first deliverable is measurement: completion rate for background work and notification delivery, broken down by manufacturer, OS version and standby bucket, so that the conversation stops being anecdotal. Then the mitigations, in order of cheapness: move timing-critical triggers to push, make the work resumable, add a vendor-aware in-app explanation that deep links into the right settings screen, and only then consider redesigning the scheduling. Anyone who proposes a foreground service as the whole answer has not read the last few years of platform changes.

The Play Console with a warning on it

Vitals are above threshold, or a policy declaration is out of date, or the target API deadline is close enough that somebody has started forwarding the email. Installs are drifting down and nobody has connected the two.

The work here is triage in a specific order. Read the actual enforcement notice rather than the summary somebody pasted into Slack. Separate crash clusters from ANR clusters, because they have different causes and ANRs are usually main-thread work or a lock nobody has looked at. Confirm mapping files are being uploaded, since half the time the crashes are unreadable and the team has been guessing. Then bump the target level properly: read the behaviour changes, fix what breaks, test on that OS version, and roll out in stages with halt criteria written down. This is a senior engagement of a few weeks, and the outcome is measurable, which makes it one of the easier ones to scope honestly.

The View-system app somebody wants rewritten in Compose

An app that works, a team that has read about Compose, and a proposal to rewrite the UI layer. Sometimes this is right. Usually the honest answer is that a rewrite delivering the same features on a different toolkit is very hard to justify to whoever signs it off, and it is the classic project that gets cancelled partway, leaving two half-systems and nobody who remembers the plan.

What we would rather do is a migration that pays as it goes. Establish the interop boundary and the theme bridge properly, decide once who owns the back stack, and convert only screens that are already being changed for product reasons. Extract logic out of the giant Activities into testable pieces first, since that work is valuable whichever toolkit wins. The engineer for this is a senior who is fluent in both systems and comfortable saying that a particular screen should stay as it is, which is a harder thing to hire for than it sounds.

The white-label app that ships to a hundred customers

One codebase, many branded builds, each with its own package name, assets, feature toggles, endpoints and store listing. The engineering questions are all about the build system: how flavours and variants are organised, how assets and configuration are supplied per brand, how CI produces and signs the whole set without a person clicking anything, and how a change is verified across brands without testing a hundred apps by hand.

This is where a candidate's Gradle experience stops being a footnote. Ask how they would keep build times sane as the variant count grows, how they detect that a change to shared code broke exactly one brand, and how they manage a hundred sets of signing credentials and listings without it becoming one person's private knowledge. It is unglamorous work and it is the difference between a product line that scales and one where every new customer costs a week.

Engagement Models for Android Work

Which shape fits depends on whether you already have an Android engineer and whether the work has an ending. We settle commercial terms with you before anybody begins, rather than expect you to infer them from a web page. Get the match wrong and a different Android engineer reaches you inside 48 hours, and you are choosing from more than one candidate rather than being sent a single name to accept or reject.

Why this is faster than recruiting the same person yourself

Companies rarely come to us because Android capability is scarce. They come because of the calendar. Hiring an Android engineer yourself, here or in your own city, runs through sourcing, screening, a technical round and an offer, and only then do you meet whatever notice period the person still owes the employer they are leaving. That last part is what catches overseas employers out, because notice here is contractual rather than customary, and it is frequently long enough to shift a start date by an entire quarter. Establish that number with any candidate before you build a plan on top of them. A person you settle on in March is very often not at a desk until June, and if your problem is an ANR rate climbing this week or a target API deadline in six weeks, June is not an answer to anything.

We do it the other way around. The engineers are already on our payroll and between projects, so there is no search phase and no leaving date to negotiate with anybody. That is the entire reason names reach you inside 48 hours of your brief and the person you pick can be committing to your repository 7 days later. There is nothing clever behind those two numbers.

None of that substitutes for your own judgement. Interview them properly, put the process death and release ownership questions to them, and turn down anyone who does not convince you. Arriving quickly is worth nothing if the wrong person arrives.